craftbeerpi4-pione/venv3/lib/python3.7/site-packages/pandas/io/formats/info.py

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2021-03-03 23:49:41 +01:00
from abc import ABCMeta, abstractmethod
import sys
from typing import IO, TYPE_CHECKING, List, Optional, Tuple, Union
from pandas._config import get_option
from pandas._typing import Dtype, FrameOrSeries
from pandas.core.indexes.api import Index
from pandas.io.formats import format as fmt
from pandas.io.formats.printing import pprint_thing
if TYPE_CHECKING:
from pandas.core.series import Series # noqa: F401
def _put_str(s: Union[str, Dtype], space: int) -> str:
"""
Make string of specified length, padding to the right if necessary.
Parameters
----------
s : Union[str, Dtype]
String to be formatted.
space : int
Length to force string to be of.
Returns
-------
str
String coerced to given length.
Examples
--------
>>> pd.io.formats.info._put_str("panda", 6)
'panda '
>>> pd.io.formats.info._put_str("panda", 4)
'pand'
"""
return str(s)[:space].ljust(space)
def _sizeof_fmt(num: Union[int, float], size_qualifier: str) -> str:
"""
Return size in human readable format.
Parameters
----------
num : int
Size in bytes.
size_qualifier : str
Either empty, or '+' (if lower bound).
Returns
-------
str
Size in human readable format.
Examples
--------
>>> _sizeof_fmt(23028, '')
'22.5 KB'
>>> _sizeof_fmt(23028, '+')
'22.5+ KB'
"""
for x in ["bytes", "KB", "MB", "GB", "TB"]:
if num < 1024.0:
return f"{num:3.1f}{size_qualifier} {x}"
num /= 1024.0
return f"{num:3.1f}{size_qualifier} PB"
class BaseInfo(metaclass=ABCMeta):
def __init__(
self,
data: FrameOrSeries,
verbose: Optional[bool] = None,
buf: Optional[IO[str]] = None,
max_cols: Optional[int] = None,
memory_usage: Optional[Union[bool, str]] = None,
null_counts: Optional[bool] = None,
):
if buf is None: # pragma: no cover
buf = sys.stdout
if memory_usage is None:
memory_usage = get_option("display.memory_usage")
self.data = data
self.verbose = verbose
self.buf = buf
self.max_cols = max_cols
self.memory_usage = memory_usage
self.null_counts = null_counts
@abstractmethod
def _get_mem_usage(self, deep: bool) -> int:
"""
Get memory usage in bytes.
Parameters
----------
deep : bool
If True, introspect the data deeply by interrogating object dtypes
for system-level memory consumption, and include it in the returned
values.
Returns
-------
mem_usage : int
Object's total memory usage in bytes.
"""
pass
@abstractmethod
def _get_ids_and_dtypes(self) -> Tuple["Index", "Series"]:
"""
Get column names and dtypes.
Returns
-------
ids : Index
DataFrame's column names.
dtypes : Series
Dtype of each of the DataFrame's columns.
"""
pass
@abstractmethod
def _verbose_repr(
self, lines: List[str], ids: "Index", dtypes: "Series", show_counts: bool
) -> None:
"""
Append name, non-null count (optional), and dtype for each column to `lines`.
Parameters
----------
lines : List[str]
Lines that will contain `info` representation.
ids : Index
The DataFrame's column names.
dtypes : Series
The DataFrame's columns' dtypes.
show_counts : bool
If True, count of non-NA cells for each column will be appended to `lines`.
"""
pass
@abstractmethod
def _non_verbose_repr(self, lines: List[str], ids: "Index") -> None:
"""
Append short summary of columns' names to `lines`.
Parameters
----------
lines : List[str]
Lines that will contain `info` representation.
ids : Index
The DataFrame's column names.
"""
pass
def info(self) -> None:
"""
Print a concise summary of a %(klass)s.
This method prints information about a %(klass)s including
the index dtype%(type_sub)s, non-null values and memory usage.
Parameters
----------
data : %(klass)s
%(klass)s to print information about.
verbose : bool, optional
Whether to print the full summary. By default, the setting in
``pandas.options.display.max_info_columns`` is followed.
buf : writable buffer, defaults to sys.stdout
Where to send the output. By default, the output is printed to
sys.stdout. Pass a writable buffer if you need to further process
the output.
%(max_cols_sub)s
memory_usage : bool, str, optional
Specifies whether total memory usage of the %(klass)s
elements (including the index) should be displayed. By default,
this follows the ``pandas.options.display.memory_usage`` setting.
True always show memory usage. False never shows memory usage.
A value of 'deep' is equivalent to "True with deep introspection".
Memory usage is shown in human-readable units (base-2
representation). Without deep introspection a memory estimation is
made based in column dtype and number of rows assuming values
consume the same memory amount for corresponding dtypes. With deep
memory introspection, a real memory usage calculation is performed
at the cost of computational resources.
null_counts : bool, optional
Whether to show the non-null counts. By default, this is shown
only if the %(klass)s is smaller than
``pandas.options.display.max_info_rows`` and
``pandas.options.display.max_info_columns``. A value of True always
shows the counts, and False never shows the counts.
Returns
-------
None
This method prints a summary of a %(klass)s and returns None.
See Also
--------
%(see_also_sub)s
Examples
--------
%(examples_sub)s
"""
lines = []
lines.append(str(type(self.data)))
lines.append(self.data.index._summary())
ids, dtypes = self._get_ids_and_dtypes()
col_count = len(ids)
if col_count == 0:
lines.append(f"Empty {type(self.data).__name__}")
fmt.buffer_put_lines(self.buf, lines)
return
# hack
max_cols = self.max_cols
if max_cols is None:
max_cols = get_option("display.max_info_columns", col_count + 1)
max_rows = get_option("display.max_info_rows", len(self.data) + 1)
if self.null_counts is None:
show_counts = (col_count <= max_cols) and (len(self.data) < max_rows)
else:
show_counts = self.null_counts
exceeds_info_cols = col_count > max_cols
if self.verbose:
self._verbose_repr(lines, ids, dtypes, show_counts)
elif self.verbose is False: # specifically set to False, not necessarily None
self._non_verbose_repr(lines, ids)
else:
if exceeds_info_cols:
self._non_verbose_repr(lines, ids)
else:
self._verbose_repr(lines, ids, dtypes, show_counts)
# groupby dtype.name to collect e.g. Categorical columns
counts = dtypes.value_counts().groupby(lambda x: x.name).sum()
collected_dtypes = [f"{k[0]}({k[1]:d})" for k in sorted(counts.items())]
lines.append(f"dtypes: {', '.join(collected_dtypes)}")
if self.memory_usage:
# append memory usage of df to display
size_qualifier = ""
if self.memory_usage == "deep":
deep = True
else:
# size_qualifier is just a best effort; not guaranteed to catch
# all cases (e.g., it misses categorical data even with object
# categories)
deep = False
if "object" in counts or self.data.index._is_memory_usage_qualified():
size_qualifier = "+"
mem_usage = self._get_mem_usage(deep=deep)
lines.append(f"memory usage: {_sizeof_fmt(mem_usage, size_qualifier)}\n")
fmt.buffer_put_lines(self.buf, lines)
class DataFrameInfo(BaseInfo):
def _get_mem_usage(self, deep: bool) -> int:
return self.data.memory_usage(index=True, deep=deep).sum()
def _get_ids_and_dtypes(self) -> Tuple["Index", "Series"]:
return self.data.columns, self.data.dtypes
def _verbose_repr(
self, lines: List[str], ids: "Index", dtypes: "Series", show_counts: bool
) -> None:
col_count = len(ids)
lines.append(f"Data columns (total {col_count} columns):")
id_head = " # "
column_head = "Column"
col_space = 2
max_col = max(len(pprint_thing(k)) for k in ids)
len_column = len(pprint_thing(column_head))
space = max(max_col, len_column) + col_space
max_id = len(pprint_thing(col_count))
len_id = len(pprint_thing(id_head))
space_num = max(max_id, len_id) + col_space
header = _put_str(id_head, space_num) + _put_str(column_head, space)
if show_counts:
counts = self.data.count()
if col_count != len(counts): # pragma: no cover
raise AssertionError(
f"Columns must equal counts ({col_count} != {len(counts)})"
)
count_header = "Non-Null Count"
len_count = len(count_header)
non_null = " non-null"
max_count = max(len(pprint_thing(k)) for k in counts) + len(non_null)
space_count = max(len_count, max_count) + col_space
count_temp = "{count}" + non_null
else:
count_header = ""
space_count = len(count_header)
len_count = space_count
count_temp = "{count}"
dtype_header = "Dtype"
len_dtype = len(dtype_header)
max_dtypes = max(len(pprint_thing(k)) for k in dtypes)
space_dtype = max(len_dtype, max_dtypes)
header += _put_str(count_header, space_count) + _put_str(
dtype_header, space_dtype
)
lines.append(header)
lines.append(
_put_str("-" * len_id, space_num)
+ _put_str("-" * len_column, space)
+ _put_str("-" * len_count, space_count)
+ _put_str("-" * len_dtype, space_dtype)
)
for i, col in enumerate(ids):
dtype = dtypes.iloc[i]
col = pprint_thing(col)
line_no = _put_str(f" {i}", space_num)
count = ""
if show_counts:
count = counts.iloc[i]
lines.append(
line_no
+ _put_str(col, space)
+ _put_str(count_temp.format(count=count), space_count)
+ _put_str(dtype, space_dtype)
)
def _non_verbose_repr(self, lines: List[str], ids: "Index") -> None:
lines.append(ids._summary(name="Columns"))